Software Sale Live

Softwares MegaODA and CTA, as well as a .pdf  of the book Maximizing Predictive Accuracy, are for sale on the Purchase page for $9.99.


 

To run MegaODA and CTA softwares within Stata refer to these pages. Dr. Ariel Linden created and published the Stata programs for these softwares.

ODA, LLC will publish instructional videos to YouTube on these softwares regularly.

Implementing ODA from Within Stata: Finding the Optimal Cut-Point of a Diagnostic Test or Index (Invited)

Ariel Linden

Linden Consulting Group, LLC

Maximizing the discriminatory accuracy of a diagnostic or screening test is paramount to ensuring that individuals with or without the disease (or disease marker) are correctly identified as such. In this paper, I describe how the new Stata package for implementing ODA can be used to determine the optimal cut-point along the continuum of test values that maximally discriminates between those with and without the disease.

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Implementing CTA from Within Stata: Using CTA to Generate Propensity Score Weights (Invited)

Ariel Linden

Linden Consulting Group, LLC

In contrast to randomized studies in which individuals have no control over their treatment assignment, participants in observational studies self-select into the treatment arm and are therefore likely to differ in their characteristics compared to those who elect not to participate. Analytic approaches using the propensity score to adjust for differences between study groups are thus popular among investigators of observational data. In this paper, I describe how the new Stata package for implementing CTA can be used to generate propensity score weights.

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Implementing CTA from Within Stata: Characterizing Participation in Observational Studies (Invited)

Ariel Linden

Linden Consulting Group, LLC

In contrast to randomized studies where individuals have no control over their treatment assignment, participants in observational studies self-select into the treatment arm and are therefore likely to differ in their characteristics from those who elect not to participate. These differences may explain part, or all, of the difference in the observed outcome. In this paper, I describe how the new Stata package for implementing CTA can identify patterns in the data that distinguish study participants from non-participants, revealing potentially complex relationships among individual characteristics that may bias the outcome analysis.

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Implementing CTA from Within Stata: Assessing the Quality of the Randomization Process in Randomized Controlled Trials (Invited)

Ariel Linden

Linden Consulting Group, LLC

In randomized controlled trials (RCT) of sufficient size, we expect the treatment and control groups to be balanced on both observed and unobserved characteristics, and any imbalances are considered to be due to chance. CTA can be used to determine whether treatment assignment can be predicted by observed pre-intervention covariates–separately or interacted with other covariates. In this paper, I describe how to assess the quality of the randomization process in RCTs using the new Stata package for implementing CTA.

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Implementing ODA from Within Stata: Identifying Structural Breaks in Single-Group Interrupted Time Series Designs (Invited)

Ariel Linden

Linden Consulting Group, LLC

In this paper, I describe how to determine if any structural breaks exist in a time series prior to the introduction of an intervention using the new Stata package for implementing ODA. Given that the internal validity of the design rests on the premise that the interruption in the time series is associated with the introduction of the intervention, treatment effects may seem less plausible if a parallel trend already exists in the time series prior to the actual intervention.

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